Overview

Dataset statistics

Number of variables13
Number of observations5238
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory572.9 KiB
Average record size in memory112.0 B

Variable types

Categorical2
Numeric11

Alerts

density is highly skewed (γ1 = 38.20577452)Skewed

Reproduction

Analysis started2023-12-12 05:26:38.742031
Analysis finished2023-12-12 05:27:02.853282
Duration24.11 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

category
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.8 KiB
white
3965 
red
1273 

Length

Max length5
Median length5
Mean length4.5139366
Min length3

Characters and Unicode

Total characters23644
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowred
2nd rowred
3rd rowred
4th rowred
5th rowred

Common Values

ValueCountFrequency (%)
white 3965
75.7%
red 1273
 
24.3%

Length

2023-12-12T10:57:02.976162image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:57:03.141610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
white 3965
75.7%
red 1273
 
24.3%

Most occurring characters

ValueCountFrequency (%)
e 5238
22.2%
w 3965
16.8%
h 3965
16.8%
i 3965
16.8%
t 3965
16.8%
r 1273
 
5.4%
d 1273
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23644
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5238
22.2%
w 3965
16.8%
h 3965
16.8%
i 3965
16.8%
t 3965
16.8%
r 1273
 
5.4%
d 1273
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 23644
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5238
22.2%
w 3965
16.8%
h 3965
16.8%
i 3965
16.8%
t 3965
16.8%
r 1273
 
5.4%
d 1273
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23644
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5238
22.2%
w 3965
16.8%
h 3965
16.8%
i 3965
16.8%
t 3965
16.8%
r 1273
 
5.4%
d 1273
 
5.4%

fixed acidity
Real number (ℝ)

Distinct106
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.7978498 × 10-16
Minimum-2.5884684
Maximum6.5532382
Zeros0
Zeros (%)0.0%
Negative3205
Negative (%)61.2%
Memory size81.8 KiB
2023-12-12T10:57:03.508739image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-2.5884684
5-th percentile-1.1529938
Q1-0.62413475
median-0.17082699
Q30.35803207
95-th percentile2.0201606
Maximum6.5532382
Range9.1417066
Interquartile range (IQR)0.98216683

Descriptive statistics

Standard deviation1.0000955
Coefficient of variation (CV)-1.1367499 × 1015
Kurtosis4.546566
Mean-8.7978498 × 10-16
Median Absolute Deviation (MAD)0.45330777
Skewness1.6534509
Sum-4.5470294 × 10-12
Variance1.0001909
MonotonicityNot monotonic
2023-12-12T10:57:03.893288image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.3219295754 271
 
5.2%
-0.4730321642 265
 
5.1%
-0.624134753 244
 
4.7%
-0.2463782809 222
 
4.2%
-0.1708269865 219
 
4.2%
-0.3974808698 215
 
4.1%
-0.01972439769 196
 
3.7%
-0.5485834586 191
 
3.6%
-0.09527569211 190
 
3.6%
-0.7752373419 179
 
3.4%
Other values (96) 3046
58.2%
ValueCountFrequency (%)
-2.588468408 1
 
< 0.1%
-2.512917113 1
 
< 0.1%
-2.28626323 2
 
< 0.1%
-2.135160641 3
 
0.1%
-2.059609347 1
 
< 0.1%
-1.984058053 1
 
< 0.1%
-1.908506758 6
 
0.1%
-1.832955464 7
 
0.1%
-1.757404169 5
 
0.1%
-1.681852875 25
0.5%
ValueCountFrequency (%)
6.553238216 1
< 0.1%
6.326584333 2
< 0.1%
6.251033039 1
< 0.1%
5.873276567 1
< 0.1%
5.344417506 1
< 0.1%
5.268866211 1
< 0.1%
5.117763623 1
< 0.1%
4.966661034 1
< 0.1%
4.891109739 1
< 0.1%
4.740007151 1
< 0.1%

volatile acidity
Real number (ℝ)

Distinct184
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1471156 × 10-16
Minimum-1.718641
Maximum20.602528
Zeros0
Zeros (%)0.0%
Negative3156
Negative (%)60.3%
Memory size81.8 KiB
2023-12-12T10:57:04.145289image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1.718641
5-th percentile-1.1269245
Q1-0.64173496
median-0.24735048
Q30.4302437
95-th percentile1.84079
Maximum20.602528
Range22.321169
Interquartile range (IQR)1.0719787

Descriptive statistics

Standard deviation1.0000955
Coefficient of variation (CV)3.1778161 × 1015
Kurtosis55.516592
Mean3.1471156 × 10-16
Median Absolute Deviation (MAD)0.49822597
Skewness3.6937573
Sum1.7024437 × 10-12
Variance1.0001909
MonotonicityNot monotonic
2023-12-12T10:57:04.385164image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.3117905667 232
 
4.4%
-0.5746859428 222
 
4.2%
-0.4421949753 219
 
4.2%
-0.3767360668 188
 
3.6%
-0.5081754743 187
 
3.6%
-0.7093313292 183
 
3.5%
-0.6417349635 180
 
3.4%
-0.8462023596 178
 
3.4%
-0.1834080135 169
 
3.2%
-0.056985576 166
 
3.2%
Other values (174) 3314
63.3%
ValueCountFrequency (%)
-1.718641038 2
 
< 0.1%
-1.680393816 1
 
< 0.1%
-1.642322444 1
 
< 0.1%
-1.566700833 6
 
0.1%
-1.529147441 4
 
0.1%
-1.491763591 9
 
0.2%
-1.454547758 3
 
0.1%
-1.41749844 31
0.6%
-1.380614153 3
 
0.1%
-1.343893432 37
0.7%
ValueCountFrequency (%)
20.60252773 1
 
< 0.1%
18.22037379 1
 
< 0.1%
4.322112896 1
 
< 0.1%
4.097288892 1
 
< 0.1%
3.905157098 1
 
< 0.1%
3.846637358 1
 
< 0.1%
3.787701103 1
 
< 0.1%
3.748175926 1
 
< 0.1%
3.668555036 1
 
< 0.1%
3.547669549 3
0.1%

citric acid
Real number (ℝ)

Distinct92
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.2450329 × 10-16
Minimum-1.9750246
Maximum30.018528
Zeros0
Zeros (%)0.0%
Negative2880
Negative (%)55.0%
Memory size81.8 KiB
2023-12-12T10:57:04.606189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1.9750246
5-th percentile-1.4163338
Q1-0.41247447
median-0.06885214
Q30.41814175
95-th percentile1.211268
Maximum30.018528
Range31.993553
Interquartile range (IQR)0.83061622

Descriptive statistics

Standard deviation1.0000955
Coefficient of variation (CV)-4.454703 × 1015
Kurtosis284.5453
Mean-2.2450329 × 10-16
Median Absolute Deviation (MAD)0.40249226
Skewness10.751897
Sum-1.1759482 × 10-12
Variance1.0001909
MonotonicityNot monotonic
2023-12-12T10:57:04.906775image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1250153824 266
 
5.1%
-0.01311599874 243
 
4.6%
-0.2386497092 237
 
4.5%
0.8747835644 232
 
4.4%
-0.3540736264 209
 
4.0%
0.09710062355 203
 
3.9%
-0.1816123221 198
 
3.8%
-0.06885214006 188
 
3.6%
-0.4713444045 186
 
3.6%
-0.2961344531 180
 
3.4%
Other values (82) 3096
59.1%
ValueCountFrequency (%)
-1.975024563 31
0.6%
-1.902814449 44
0.8%
-1.831308836 27
0.5%
-1.760494108 36
0.7%
-1.690357044 24
0.5%
-1.620884796 26
0.5%
-1.55206488 30
0.6%
-1.483885158 37
0.7%
-1.416333829 36
0.7%
-1.349399416 42
0.8%
ValueCountFrequency (%)
30.01852844 1
 
< 0.1%
26.79670208 1
 
< 0.1%
18.27309056 1
 
< 0.1%
15.52686213 1
 
< 0.1%
5.122460428 1
 
< 0.1%
3.830130769 1
 
< 0.1%
3.032307742 6
0.1%
2.995569484 1
 
< 0.1%
2.694839411 1
 
< 0.1%
2.578806371 1
 
< 0.1%

residual sugar
Real number (ℝ)

Distinct319
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.5192972 × 10-16
Minimum-0.95524815
Maximum15.312761
Zeros0
Zeros (%)0.0%
Negative3323
Negative (%)63.4%
Memory size81.8 KiB
2023-12-12T10:57:05.238539image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-0.95524815
5-th percentile-0.85015765
Q1-0.70303095
median-0.49284996
Q30.51601882
95-th percentile1.9662677
Maximum15.312761
Range16.268009
Interquartile range (IQR)1.2190498

Descriptive statistics

Standard deviation1.0000955
Coefficient of variation (CV)-6.5826191 × 1015
Kurtosis24.736928
Mean-1.5192972 × 10-16
Median Absolute Deviation (MAD)0.32578054
Skewness2.7650322
Sum-6.8656192 × 10-13
Variance1.0001909
MonotonicityNot monotonic
2023-12-12T10:57:05.491230image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.6609947551 196
 
3.7%
-0.7450671535 195
 
3.7%
-0.7871033527 188
 
3.6%
-0.7030309543 186
 
3.6%
-0.8291395518 169
 
3.2%
-0.7660852531 149
 
2.8%
-0.6189585559 144
 
2.7%
-0.6820128547 144
 
2.7%
-0.6399766555 143
 
2.7%
-0.7240490539 143
 
2.7%
Other values (309) 3581
68.4%
ValueCountFrequency (%)
-0.9552481494 1
 
< 0.1%
-0.9342300498 7
 
0.1%
-0.9132119502 22
 
0.4%
-0.8921938506 36
 
0.7%
-0.8816848008 3
 
0.1%
-0.871175751 77
1.5%
-0.8606667012 1
 
< 0.1%
-0.8501576514 127
2.4%
-0.8396486016 3
 
0.1%
-0.8291395518 169
3.2%
ValueCountFrequency (%)
15.31276093 1
< 0.1%
14.68221795 1
< 0.1%
12.74855278 1
< 0.1%
6.27497811 1
< 0.1%
5.560362723 1
< 0.1%
4.393858196 1
< 0.1%
3.857896657 1
< 0.1%
3.66873376 1
< 0.1%
3.542625163 1
< 0.1%
3.290407968 2
< 0.1%

chlorides
Real number (ℝ)

Distinct216
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.387467 × 10-16
Minimum-29.983177
Maximum24.812124
Zeros0
Zeros (%)0.0%
Negative3267
Negative (%)62.4%
Memory size81.8 KiB
2023-12-12T10:57:05.761202image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-29.983177
5-th percentile-0.83315034
Q1-0.44728125
median-0.1545461
Q30.33391726
95-th percentile1.1426202
Maximum24.812124
Range54.795301
Interquartile range (IQR)0.78119851

Descriptive statistics

Standard deviation1.0000955
Coefficient of variation (CV)4.1889394 × 1015
Kurtosis335.84853
Mean2.387467 × 10-16
Median Absolute Deviation (MAD)0.3629284
Skewness3.159432
Sum1.3820056 × 10-12
Variance1.0001909
MonotonicityNot monotonic
2023-12-12T10:57:05.988560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.5184911241 164
 
3.1%
-0.2476112498 157
 
3.0%
-0.185115953 157
 
3.0%
-0.3120302267 155
 
3.0%
-0.3785281226 152
 
2.9%
-0.1545461043 148
 
2.8%
-0.4472812471 142
 
2.7%
-0.1244068527 142
 
2.7%
-0.06536154333 141
 
2.7%
-0.5923897334 137
 
2.6%
Other values (206) 3743
71.5%
ValueCountFrequency (%)
-29.98317726 1
 
< 0.1%
-1.967450144 1
 
< 0.1%
-1.71919261 2
 
< 0.1%
-1.645779316 1
 
< 0.1%
-1.576040619 4
0.1%
-1.509548583 3
 
0.1%
-1.445949837 5
0.1%
-1.384948802 5
0.1%
-1.326295437 8
0.2%
-1.26977612 7
0.1%
ValueCountFrequency (%)
24.81212357 1
< 0.1%
24.14720321 1
< 0.1%
21.93954423 1
< 0.1%
5.628670038 1
< 0.1%
5.623177215 1
< 0.1%
4.766351054 1
< 0.1%
4.746607367 2
< 0.1%
4.460795459 1
< 0.1%
4.411338663 2
< 0.1%
4.404228115 2
< 0.1%

free sulfur dioxide
Real number (ℝ)

Distinct137
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum-1.6472258
Maximum14.498845
Zeros0
Zeros (%)0.0%
Negative2885
Negative (%)55.1%
Memory size81.8 KiB
2023-12-12T10:57:06.245868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1.6472258
5-th percentile-1.3669121
Q1-0.75022188
median-0.13353166
Q30.59528405
95-th percentile1.716539
Maximum14.498845
Range16.146071
Interquartile range (IQR)1.3455059

Descriptive statistics

Standard deviation1.0000955
Coefficient of variation (CV)nan
Kurtosis9.6782179
Mean0
Median Absolute Deviation (MAD)0.67275296
Skewness1.386297
Sum1.9939606 × 10-13
Variance1.0001909
MonotonicityNot monotonic
2023-12-12T10:57:06.505201image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.07746891244 161
 
3.1%
-1.366912095 139
 
2.7%
-0.2456571537 131
 
2.5%
-0.8623473716 126
 
2.4%
-0.3577826479 126
 
2.4%
0.03465658172 125
 
2.4%
0.202844823 123
 
2.3%
-0.7502218774 121
 
2.3%
-0.4138453949 120
 
2.3%
-0.1335316595 116
 
2.2%
Other values (127) 3950
75.4%
ValueCountFrequency (%)
-1.647225831 2
 
< 0.1%
-1.591163084 2
 
< 0.1%
-1.535100337 43
 
0.8%
-1.47903759 42
 
0.8%
-1.422974842 106
2.0%
-1.394943469 1
 
< 0.1%
-1.366912095 139
2.7%
-1.310849348 73
1.4%
-1.254786601 70
1.3%
-1.198723854 77
1.5%
ValueCountFrequency (%)
14.49884533 1
< 0.1%
6.50990387 1
< 0.1%
6.061401893 1
< 0.1%
5.64093129 1
< 0.1%
5.472743049 1
< 0.1%
5.24849206 1
< 0.1%
5.16439794 1
< 0.1%
4.940146951 1
< 0.1%
4.856052831 1
< 0.1%
4.631801843 1
< 0.1%

total sulfur dioxide
Real number (ℝ)

Distinct275
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.1937335 × 10-16
Minimum-1.9610509
Maximum5.7891327
Zeros0
Zeros (%)0.0%
Negative2536
Negative (%)48.4%
Memory size81.8 KiB
2023-12-12T10:57:06.756987image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1.9610509
5-th percentile-1.7110449
Q1-0.67530611
median0.030067736
Q30.69972645
95-th percentile1.6104623
Maximum5.7891327
Range7.7501836
Interquartile range (IQR)1.3750326

Descriptive statistics

Standard deviation1.0000955
Coefficient of variation (CV)-8.3778789 × 1015
Kurtosis-0.23284245
Mean-1.1937335 × 10-16
Median Absolute Deviation (MAD)0.68751628
Skewness0.050909792
Sum-6.3327121 × 10-13
Variance1.0001909
MonotonicityNot monotonic
2023-12-12T10:57:07.069307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0860064417 53
 
1.0%
-0.05029131007 50
 
1.0%
0.1104267823 49
 
0.9%
-0.03243374425 48
 
0.9%
-0.3181547973 48
 
0.9%
0.2175721772 46
 
0.9%
0.0211389532 44
 
0.8%
0.1461419139 44
 
0.8%
-0.1038640075 44
 
0.8%
0.1639994797 43
 
0.8%
Other values (265) 4769
91.0%
ValueCountFrequency (%)
-1.961050852 2
 
< 0.1%
-1.943193287 4
 
0.1%
-1.925335721 9
 
0.2%
-1.907478155 12
0.2%
-1.889620589 24
0.5%
-1.871763023 17
0.3%
-1.853905458 19
0.4%
-1.836047892 20
0.4%
-1.818190326 23
0.4%
-1.80033276 29
0.6%
ValueCountFrequency (%)
5.789132712 1
< 0.1%
4.476601624 1
< 0.1%
4.074806393 1
< 0.1%
3.521221853 1
< 0.1%
3.423005241 1
< 0.1%
3.342646195 1
< 0.1%
3.181928103 1
< 0.1%
3.092640274 1
< 0.1%
2.967637313 1
< 0.1%
2.89620705 1
< 0.1%

density
Real number (ℝ)

SKEWED 

Distinct992
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.5269399 × 10-16
Minimum-0.058013801
Maximum46.732011
Zeros0
Zeros (%)0.0%
Negative5206
Negative (%)99.4%
Memory size81.8 KiB
2023-12-12T10:57:07.419611image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-0.058013801
5-th percentile-0.046994256
Q1-0.037480379
median-0.027756441
Q3-0.018968731
95-th percentile-0.0092471064
Maximum46.732011
Range46.790025
Interquartile range (IQR)0.018511648

Descriptive statistics

Standard deviation1.0000955
Coefficient of variation (CV)-2.8355898 × 1015
Kurtosis1514.6777
Mean-3.5269399 × 10-16
Median Absolute Deviation (MAD)0.0091896628
Skewness38.205775
Sum-1.8189894 × 10-12
Variance1.0001909
MonotonicityNot monotonic
2023-12-12T10:57:07.739676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.03828620239 60
 
1.1%
-0.01736103852 58
 
1.1%
-0.0350633935 53
 
1.0%
-0.02138087404 53
 
1.0%
-0.014146619 52
 
1.0%
-0.03264713576 52
 
1.0%
-0.01575366784 50
 
1.0%
-0.01896873115 50
 
1.0%
-0.03345247419 50
 
1.0%
-0.02298937213 46
 
0.9%
Other values (982) 4714
90.0%
ValueCountFrequency (%)
-0.05801380091 1
< 0.1%
-0.05793301661 1
< 0.1%
-0.0575694973 1
< 0.1%
-0.05684250806 1
< 0.1%
-0.05676173554 2
< 0.1%
-0.05660019295 2
< 0.1%
-0.05611558467 1
< 0.1%
-0.0554694858 1
< 0.1%
-0.05526759057 1
< 0.1%
-0.05466193535 1
< 0.1%
ValueCountFrequency (%)
46.73201097 1
 
< 0.1%
35.03685499 1
 
< 0.1%
31.39336648 1
 
< 0.1%
28.86737045 1
 
< 0.1%
0.556145941 9
0.2%
0.1488135111 1
 
< 0.1%
0.03511371412 1
 
< 0.1%
0.008678876056 1
 
< 0.1%
0.006715788135 1
 
< 0.1%
0.006515446043 2
 
< 0.1%

pH
Real number (ℝ)

Distinct106
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1607532 × 10-15
Minimum-3.3857615
Maximum4.0608841
Zeros0
Zeros (%)0.0%
Negative2676
Negative (%)51.1%
Memory size81.8 KiB
2023-12-12T10:57:07.980949image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-3.3857615
5-th percentile-1.5597704
Q1-0.69104384
median-0.041290567
Q30.65584721
95-th percentile1.6990718
Maximum4.0608841
Range7.4466455
Interquartile range (IQR)1.346891

Descriptive statistics

Standard deviation1.0000955
Coefficient of variation (CV)8.6159181 × 1014
Kurtosis0.22494184
Mean1.1607532 × 10-15
Median Absolute Deviation (MAD)0.64975327
Skewness0.23823043
Sum6.0400573 × 10-12
Variance1.0001909
MonotonicityNot monotonic
2023-12-12T10:57:08.226874image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.3642147716 158
 
3.0%
0.02283386305 157
 
3.0%
-0.4944725391 149
 
2.8%
-0.4292651868 144
 
2.7%
-0.1055674922 142
 
2.7%
0.1506281174 142
 
2.7%
-0.234581744 138
 
2.6%
-0.1699976405 135
 
2.6%
-0.6253610973 130
 
2.5%
-0.2993205401 127
 
2.4%
Other values (96) 3816
72.9%
ValueCountFrequency (%)
-3.38576146 1
 
< 0.1%
-3.240836531 2
 
< 0.1%
-3.024895826 1
 
< 0.1%
-2.881887903 2
 
< 0.1%
-2.810666691 3
 
0.1%
-2.668784819 1
 
< 0.1%
-2.598122205 3
 
0.1%
-2.527643849 1
 
< 0.1%
-2.457348791 6
0.1%
-2.387236081 8
0.2%
ValueCountFrequency (%)
4.060884052 2
< 0.1%
3.78366691 2
< 0.1%
3.615961504 1
 
< 0.1%
3.559827664 1
 
< 0.1%
3.503576999 2
< 0.1%
3.390723247 1
 
< 0.1%
3.334119175 2
< 0.1%
3.277396312 2
< 0.1%
3.220554157 4
0.1%
3.163592209 3
0.1%

sulphates
Real number (ℝ)

Distinct114
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8516434 × 10-16
Minimum-46.049893
Maximum38.462554
Zeros0
Zeros (%)0.0%
Negative3022
Negative (%)57.7%
Memory size81.8 KiB
2023-12-12T10:57:08.485074image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-46.049893
5-th percentile-0.70712766
Q1-0.37642678
median-0.082470442
Q30.24823044
95-th percentile0.98312128
Maximum38.462554
Range84.512447
Interquartile range (IQR)0.62465721

Descriptive statistics

Standard deviation1.0000955
Coefficient of variation (CV)5.4011234 × 1015
Kurtosis1275.8009
Mean1.8516434 × 10-16
Median Absolute Deviation (MAD)0.29395634
Skewness-7.4392109
Sum9.9831254 × 10-13
Variance1.0001909
MonotonicityNot monotonic
2023-12-12T10:57:08.737022image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1192149843 211
 
4.0%
-0.2661931523 194
 
3.7%
0.02776318381 192
 
3.7%
-0.3396822363 183
 
3.5%
-0.1927040683 171
 
3.3%
-0.5601494884 163
 
3.1%
-0.2294486103 159
 
3.0%
-0.1559595263 158
 
3.0%
-0.04572590022 156
 
3.0%
-0.3029376943 153
 
2.9%
Other values (104) 3498
66.8%
ValueCountFrequency (%)
-46.0498925 1
 
< 0.1%
-1.148062161 1
 
< 0.1%
-1.111317619 1
 
< 0.1%
-1.037828535 4
 
0.1%
-1.001083993 3
 
0.1%
-0.9643394506 10
 
0.2%
-0.9275949086 12
 
0.2%
-0.8908503666 12
 
0.2%
-0.8541058246 25
0.5%
-0.8173612825 31
0.6%
ValueCountFrequency (%)
38.46255413 1
 
< 0.1%
5.392466318 1
 
< 0.1%
5.318977234 1
 
< 0.1%
5.208743608 2
< 0.1%
3.996173721 1
 
< 0.1%
3.959429179 1
 
< 0.1%
3.885940095 1
 
< 0.1%
3.775706469 1
 
< 0.1%
3.040815629 3
0.1%
2.967326545 1
 
< 0.1%

alcohol
Real number (ℝ)

Distinct111
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.8821712 × 10-16
Minimum-2.1430242
Maximum3.6783392
Zeros0
Zeros (%)0.0%
Negative2945
Negative (%)56.2%
Memory size81.8 KiB
2023-12-12T10:57:08.967464image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-2.1430242
5-th percentile-1.2993483
Q1-0.8775104
median-0.11820213
Q30.72547372
95-th percentile1.8222523
Maximum3.6783392
Range5.8213634
Interquartile range (IQR)1.6029841

Descriptive statistics

Standard deviation1.0000955
Coefficient of variation (CV)-3.4699378 × 1015
Kurtosis-0.54605237
Mean-2.8821712 × 10-16
Median Absolute Deviation (MAD)0.75930826
Skewness0.54772516
Sum-1.6042723 × 10-12
Variance1.0001909
MonotonicityNot monotonic
2023-12-12T10:57:09.683248image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.8775103978 290
 
5.5%
-0.9618779828 256
 
4.9%
-1.130613153 204
 
3.9%
-0.4556724728 197
 
3.8%
-0.03383454779 195
 
3.7%
0.3880033772 174
 
3.3%
-0.6244076428 172
 
3.3%
-1.046245568 164
 
3.1%
-0.1182021328 164
 
3.1%
-0.2869373028 157
 
3.0%
Other values (101) 3265
62.3%
ValueCountFrequency (%)
-2.143024173 2
 
< 0.1%
-1.805553833 4
 
0.1%
-1.721186248 10
 
0.2%
-1.636818663 16
 
0.3%
-1.552451078 48
 
0.9%
-1.468083493 68
1.3%
-1.383715908 58
1.1%
-1.299348323 139
2.7%
-1.25716453 1
 
< 0.1%
-1.214980738 131
2.5%
ValueCountFrequency (%)
3.678339192 1
 
< 0.1%
3.087766097 1
 
< 0.1%
2.96121472 1
 
< 0.1%
2.919030927 9
0.2%
2.834663342 3
 
0.1%
2.750295757 1
 
< 0.1%
2.665928172 5
0.1%
2.581560587 10
0.2%
2.553438059 1
 
< 0.1%
2.539376795 1
 
< 0.1%

quality
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size81.8 KiB
0.2429442908121419
2288 
-1.1283296265089782
1939 
1.614218208133262
1011 

Length

Max length19
Median length18
Mean length18.177167
Min length17

Characters and Unicode

Total characters95212
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1.1283296265089782
2nd row0.2429442908121419
3rd row-1.1283296265089782
4th row1.614218208133262
5th row-1.1283296265089782

Common Values

ValueCountFrequency (%)
0.2429442908121419 2288
43.7%
-1.1283296265089782 1939
37.0%
1.614218208133262 1011
19.3%

Length

2023-12-12T10:57:09.949776image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:57:10.231549image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.2429442908121419 2288
43.7%
1.1283296265089782 1939
37.0%
1.614218208133262 1011
19.3%

Most occurring characters

ValueCountFrequency (%)
2 20952
22.0%
1 14786
15.5%
9 10742
11.3%
4 10163
10.7%
8 10127
10.6%
0 7526
 
7.9%
6 5900
 
6.2%
. 5238
 
5.5%
3 3961
 
4.2%
- 1939
 
2.0%
Other values (2) 3878
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 88035
92.5%
Other Punctuation 5238
 
5.5%
Dash Punctuation 1939
 
2.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 20952
23.8%
1 14786
16.8%
9 10742
12.2%
4 10163
11.5%
8 10127
11.5%
0 7526
 
8.5%
6 5900
 
6.7%
3 3961
 
4.5%
5 1939
 
2.2%
7 1939
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 5238
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1939
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 95212
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 20952
22.0%
1 14786
15.5%
9 10742
11.3%
4 10163
10.7%
8 10127
10.6%
0 7526
 
7.9%
6 5900
 
6.2%
. 5238
 
5.5%
3 3961
 
4.2%
- 1939
 
2.0%
Other values (2) 3878
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 95212
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 20952
22.0%
1 14786
15.5%
9 10742
11.3%
4 10163
10.7%
8 10127
10.6%
0 7526
 
7.9%
6 5900
 
6.2%
. 5238
 
5.5%
3 3961
 
4.2%
- 1939
 
2.0%
Other values (2) 3878
 
4.1%

Interactions

2023-12-12T10:57:00.283581image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:39.105197image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:40.900909image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:43.669364image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:45.574238image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:47.647038image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:49.512985image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:51.921462image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:53.938772image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:55.952509image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:58.068572image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:57:00.430788image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:39.267290image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:41.072429image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:43.833670image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:45.774605image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:47.810210image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:49.935269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:52.092691image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:54.088481image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:56.134911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:58.234113image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:57:00.599980image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:39.419166image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:41.219678image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:43.978933image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:45.950226image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:47.970637image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:50.096683image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:52.311858image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:54.252308image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:56.288844image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:58.415998image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:57:00.781754image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:39.583216image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:41.374537image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:44.152934image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:46.125888image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:48.136785image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:50.302853image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:52.508821image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:54.521650image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:56.453326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:58.891728image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:57:00.965083image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:39.762250image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:41.638468image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:44.336888image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:46.356587image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:48.314592image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:50.492098image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:52.695312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:54.687074image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:56.703900image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:59.084717image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:57:01.112292image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:39.910551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:42.571351image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:44.502522image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:46.521891image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:48.468343image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:50.734474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:52.841107image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:54.889868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:56.858850image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:59.239553image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:57:01.364040image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:40.082511image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:42.755960image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:44.720145image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:46.731068image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:48.680702image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:50.925718image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:53.056005image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:55.103286image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:57.065919image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:59.432391image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:57:01.533318image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:40.243820image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:42.977087image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:44.921643image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:46.956100image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:48.846309image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:51.128107image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:53.257751image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:55.270995image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:57.267755image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:59.635932image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:57:01.704267image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:40.390571image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:43.139855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:45.086099image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:47.132643image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:49.019203image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:51.324883image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:53.424069image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:55.436928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:57.522188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:59.781500image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:57:01.955727image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:40.578980image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:43.304368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:45.251390image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:47.298013image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:49.194808image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:51.513095image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:53.625037image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:55.621055image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:57.716029image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:59.967854image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:57:02.109803image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:40.738720image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:43.513119image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:45.402680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:47.487007image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:49.353286image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:51.725850image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:53.789278image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:55.769327image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:56:57.868286image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-12T10:57:00.125933image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2023-12-12T10:57:02.342675image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:57:02.698659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

categoryfixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
0red0.4335832.325168-1.760494-0.5979400.919305-0.862347-1.103888-0.0181650.2778210.431953-0.624408-1.128330
1red3.002327-0.3117911.211268-0.6820130.566869-0.750222-0.996742-0.014147-0.3642150.174741-0.6244080.242944
2red0.5091351.535952-1.620885-0.7450670.429784-0.862347-1.014600-0.0205770.530425-0.266193-0.961878-1.128330
3red0.4335831.431793-1.902814-0.6609950.522014-1.198724-1.746760-0.0189690.9049590.137997-0.8775101.614218
4red0.2069291.0015400.2056840.2007470.476332-0.750222-0.246725-0.0149500.8428960.983121-0.033835-1.128330
5red-0.3974811.431793-1.483885-0.7030311.014583-0.862347-0.907454-0.0225870.4044180.027763-1.130613-1.128330
6red0.4335831.587544-0.181612-0.7450671.315922-1.198724-1.550327-0.0165570.2778213.775706-1.214981-1.128330
7red1.2646481.638816-0.834854-0.2826692.2115831.2119740.521151-0.011737-0.3642151.277078-1.130613-1.128330
8red1.2646481.638816-0.773003-0.2616512.1351881.1559120.574723-0.011737-0.2993211.460800-1.130613-1.128330
9red0.962442-0.3117911.211268-0.7030310.9193050.258908-0.228867-0.0185670.5304250.799399-0.0338351.614218
categoryfixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
5232white-0.624135-0.442195-0.4713440.2638020.186989-0.1895940.146142-0.0451390.022834-0.1559601.7378851.614218
5234white-0.9263400.0675361.6666482.260521-0.185116-0.0774690.860445-0.014548-0.494473-0.119215-1.4680830.242944
5236white1.9446090.1902120.7257091.125544-0.447281-0.077469-0.586018-0.023794-2.177983-0.854106-0.371305-1.128330
5237white-0.9263400.0675361.6666482.260521-0.185116-0.0774690.860445-0.014548-0.494473-0.119215-1.4680831.614218
5238white0.131378-0.5081750.2593791.7560870.208382-0.0774691.360456-0.016155-1.424289-0.339682-1.214981-1.128330
5239white-0.095276-1.417498-0.0131160.93638124.147203-0.0774690.824729-0.0213811.151796-0.449916-0.961878-1.128330
5240white-0.926340-0.777484-0.4713441.461833-0.065362-0.0774690.860445-0.0181650.780690-0.523405-0.961878-1.128330
5241white0.206929-0.1516210.4181422.8910640.182446-0.0774690.967590-0.006116-1.491945-0.266193-1.299348-1.128330
5242white0.055827-1.343893-0.0131161.945250-29.983177-0.077469-0.121722-0.016557-0.105567-0.670383-1.1306130.242944
5243white-0.095276-1.417498-0.0131160.9363810.048165-0.0774690.824729-0.0213811.151796-0.449916-0.961878-1.128330